license: apache-2.0
library_name: transformers
pipeline_tag: feature-extraction
E2Rank: Your Text Embedding can Also be an Effective and Efficient Listwise Reranker
We introduce $\textrm{E}^2\text{Rank}$, meaning Efficient Embedding-based Ranking (also meaning Embedding-to-Rank), which extends a single text embedding model to perform both high-quality retrieval and listwise reranking, thereby achieving strong effectiveness with remarkable efficiency.
This model is presented in the paper: $\text{E}^2\text{Rank}$: Your Text Embedding can Also be an Effective and Efficient Listwise Reranker.
Project Page: https://alibaba-nlp.github.io/E2Rank/ Code: https://github.com/Alibaba-NLP/E2Rank
(a) Overview of E2Rank. (b) Average reranking performance on the BEIR benchmark, E2Rank outperforms other baselines. (c) Reranking latency per query on the Covid dataset, E2Rank can achieve several times the acceleration compared with RankQwen3.
Introduction
We introduce $\textrm{E}^2\text{Rank}$, meaning Efficient Embedding-based Ranking (also meaning Embedding-to-Rank), which extends a single text embedding model to perform both high-quality retrieval and listwise reranking, thereby achieving strong effectiveness with remarkable efficiency.
By applying cosine similarity between the query and document embeddings as a unified ranking function, the listwise ranking prompt, which is constructed from the original query and its candidate documents, serves as an enhanced query enriched with signals from the top-K documents, akin to pseudo-relevance feedback (PRF) in traditional retrieval models. This design preserves the efficiency and representational quality of the base embedding model while significantly improving its reranking performance.
Empirically, E2Rank achieves state-of-the-art results on the BEIR reranking benchmark and demonstrates competitive performance on the reasoning-intensive BRIGHT benchmark, with very low reranking latency. We also show that the ranking training process improves embedding performance on the MTEB benchmark. Our findings indicate that a single embedding model can effectively unify retrieval and reranking, offering both computational efficiency and competitive ranking accuracy.
Our work highlights the potential of single embedding models to serve as unified retrieval-reranking engines, offering a practical, efficient, and accurate alternative to complex multi-stage ranking systems.
Abstract
Text embedding models serve as a fundamental component in real-world search applications. By mapping queries and documents into a shared embedding space, they deliver competitive retrieval performance with high efficiency. However, their ranking fidelity remains limited compared to dedicated rerankers, especially recent LLM-based listwise rerankers, which capture fine-grained query-document and document-document interactions. In this paper, we propose a simple yet effective unified framework $\text{E}^2\text{Rank}$, means Efficient Embedding-based Ranking (also means Embedding-to-Rank), which extends a single text embedding model to perform both high-quality retrieval and listwise reranking through continued training under a listwise ranking objective, thereby achieving strong effectiveness with remarkable efficiency. By applying cosine similarity between the query and document embeddings as a unified ranking function, the listwise ranking prompt, which is constructed from the original query and its candidate documents, serves as an enhanced query enriched with signals from the top-K documents, akin to pseudo-relevance feedback (PRF) in traditional retrieval models. This design preserves the efficiency and representational quality of the base embedding model while significantly improving its reranking performance. Empirically, $\textrm{E}^2\text{Rank}$ achieves state-of-the-art results on the BEIR reranking benchmark and demonstrates competitive performance on the reasoning-intensive BRIGHT benchmark, with very low reranking latency. We also show that the ranking training process improves embedding performance on the MTEB benchmark. Our findings indicate that a single embedding model can effectively unify retrieval and reranking, offering both computational efficiency and competitive ranking accuracy.
Usage
Embedding Model
The usage of E2Rank as an embedding model is similar to Qwen3-Embedding. The only difference is that Qwen3-Embedding will automatically append an EOS token, while E2Rank requires users to manually append the special token <|endoftext|> at the end of each input text.
The following code demonstrates how to use Alibaba-NLP/E2Rank-0.6B (or other E2Rank models) with the Hugging Face transformers library to obtain embeddings.
# Requires transformers>=4.51.0
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\
Query:{query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'What is the capital of China?'),
get_detailed_instruct(task, 'Explain gravity')
]
# No need to add instruction for retrieval documents
documents = [
"The capital of China is Beijing.",
"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
]
input_texts = queries + documents
input_texts = [t + "<|endoftext|>" for t in input_texts]
tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/E2Rank-0.6B', padding_side='left')
model = AutoModel.from_pretrained('Alibaba-NLP/E2Rank-0.6B')
max_length = 8192
# Tokenize the input texts
batch_dict = tokenizer(
input_texts,
padding=True,
truncation=True,
max_length=max_length,
return_tensors="pt",
)
batch_dict.to(model.device)
with torch.no_grad():
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T)
print(scores.tolist())
# [[0.5950675010681152, 0.030417663976550102], [0.061970409005880356, 0.562691330909729]]
Citation
If this work is helpful, please kindly cite as:
@misc{liu2025e2rank,
title={E2Rank: Your Text Embedding can Also be an Effective and Efficient Listwise Reranker},
author={Qi Liu and Yanzhao Zhang and Mingxin Li and Dingkun Long and Pengjun Xie and Jiaxin Mao},
year={2025},
eprint={2510.22733},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2510.22733},
}